Class PolynomialFeaturesScikitsLearnNode

Generate polynomial and interaction features.
This node has been automatically generated by wrapping the ``sklearn.preprocessing.data.PolynomialFeatures`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
Generate a new feature matrix consisting of all polynomial combinations
of the features with degree less than or equal to the specified degree.
For example, if an input sample is two dimensional and of the form
[a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2].
**Parameters**
degree : integer
The degree of the polynomial features. Default = 2.
interaction_only : boolean, default = False
If true, only interaction features are produced: features that are
products of at most ``degree`` *distinct* input features (so not
``x[1] ** 2``, ``x[0] * x[2] ** 3``, etc.).
include_bias : boolean
If True (default), then include a bias column, the feature in which
all polynomial powers are zero (i.e. a column of ones - acts as an
intercept term in a linear model).
**Examples**
>>> X = np.arange(6).reshape(3, 2)
>>> X
array([[0, 1],
[2, 3],
[4, 5]])
>>> poly = PolynomialFeatures(2)
>>> poly.fit_transform(X)
array([[ 1., 0., 1., 0., 0., 1.],
[ 1., 2., 3., 4., 6., 9.],
[ 1., 4., 5., 16., 20., 25.]])
>>> poly = PolynomialFeatures(interaction_only=True)
>>> poly.fit_transform(X)
array([[ 1., 0., 1., 0.],
[ 1., 2., 3., 6.],
[ 1., 4., 5., 20.]])
**Attributes**
``powers_`` : array, shape (n_input_features, n_output_features)
powers_[i, j] is the exponent of the jth input in the ith output.
``n_input_features_`` : int
The total number of input features.
``n_output_features_`` : int
The total number of polynomial output features. The number of output
features is computed by iterating over all suitably sized combinations
of input features.
**Notes**
Be aware that the number of features in the output array scales
polynomially in the number of features of the input array, and
exponentially in the degree. High degrees can cause overfitting.
See :ref:`examples/linear_model/plot_polynomial_interpolation.py
<example_linear_model_plot_polynomial_interpolation.py>`

stop_training(self,
**kwargs)
This node has been automatically generated by wrapping the sklearn.preprocessing.data.PolynomialFeatures class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.

Generate polynomial and interaction features.
This node has been automatically generated by wrapping the ``sklearn.preprocessing.data.PolynomialFeatures`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
Generate a new feature matrix consisting of all polynomial combinations
of the features with degree less than or equal to the specified degree.
For example, if an input sample is two dimensional and of the form
[a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2].
**Parameters**
degree : integer
The degree of the polynomial features. Default = 2.
interaction_only : boolean, default = False
If true, only interaction features are produced: features that are
products of at most ``degree`` *distinct* input features (so not
``x[1] ** 2``, ``x[0] * x[2] ** 3``, etc.).
include_bias : boolean
If True (default), then include a bias column, the feature in which
all polynomial powers are zero (i.e. a column of ones - acts as an
intercept term in a linear model).
**Examples**
>>> X = np.arange(6).reshape(3, 2)
>>> X
array([[0, 1],
[2, 3],
[4, 5]])
>>> poly = PolynomialFeatures(2)
>>> poly.fit_transform(X)
array([[ 1., 0., 1., 0., 0., 1.],
[ 1., 2., 3., 4., 6., 9.],
[ 1., 4., 5., 16., 20., 25.]])
>>> poly = PolynomialFeatures(interaction_only=True)
>>> poly.fit_transform(X)
array([[ 1., 0., 1., 0.],
[ 1., 2., 3., 6.],
[ 1., 4., 5., 20.]])
**Attributes**
``powers_`` : array, shape (n_input_features, n_output_features)
powers_[i, j] is the exponent of the jth input in the ith output.
``n_input_features_`` : int
The total number of input features.
``n_output_features_`` : int
The total number of polynomial output features. The number of output
features is computed by iterating over all suitably sized combinations
of input features.
**Notes**
Be aware that the number of features in the output array scales
polynomially in the number of features of the input array, and
exponentially in the degree. High degrees can cause overfitting.
See :ref:`examples/linear_model/plot_polynomial_interpolation.py
<example_linear_model_plot_polynomial_interpolation.py>`

_stop_training(self,
**kwargs)

execute(self,
x)

Transform data to polynomial features

This node has been automatically generated by wrapping the sklearn.preprocessing.data.PolynomialFeatures class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.

Parameters

X :array-like, shape [n_samples, n_features]

The data to transform, row by row.

Returns

XP :np.ndarray shape [n_samples, NP]

The matrix of features, where NP is the number of polynomial
features generated from the combination of inputs.

is_trainable()Static Method

stop_training(self,
**kwargs)

This node has been automatically generated by wrapping the sklearn.preprocessing.data.PolynomialFeatures class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.